Research on Flexible End-Effectors with Humanoid Grasp Function for Small Spherical Fruit Picking

Abstract

The rapid, stable, and undamaged picking of small-sized spherical fruits are one of the key technologies to improve the level of intelligent picking robots and reduce grading operations. Cherry tomatoes were selected as the research object in this work. Picking strategies of two-stage “Holding-Rotating” and finger-end grasping were determined. The end-effector was designed to separate the fruit from the stalk based on the linear motion of the constraint part and the rotating gripper. This work first studied the human hand-grasping of cherry tomatoes and designed the fingers with sinusoidal characteristics. The mathematical model of a single finger of the gripper was established. The structural parameters of the gripper were determined to meet the requirements of the grabbing range from 0 to 61.6 mm. Based on the simulation model, the constraint part was set to 6 speeds, and the fruit sizes were set to 20 mm, 30 mm, and 40 mm, respectively. When the speed was 0.08m/s, the results showed that the grabbing time was 0.5381 s, 0.387 s, and 0.2761 s, respectively, and the maximum grabbing force was 0.9717 N, 3.5077 N, and 4.0003 N now of clamping, respectively. It met the picking requirements of high speed and low loss. The criterions of two-index stability and undamaged were proposed, including the grasping index of the fixed value and the slip detection of variance to mean ratio. Therefore, the control strategy and algorithm based on two-stage and two-index for rapid, stable, and non-destructive harvesting of small fruit were proposed. The results of the picking experiment for seventy-two cherry tomatoes showed that the picking success rate was 95.82%, the average picking time was 4.86 s, the picking damage rate was 2.90%, the browning rate was 2.90% in 72 h, and the wrinkling rate was 1.49% in 72 h, which can meet the actual small spherical fruit picking requirements. The research will provide an idea for the flexible end-effectors with humanoid grasp function and provides a theoretical reference for small spherical fruit picking.

A Distance Measurement Approach for Large Fruit Picking with Single Camera

“…The number of pixels in the target image was calculated by Photoshop's lasso tool. Then the information about square checkerboards and circular checkerboards (distance, area, and number of pixels) was loaded into MATLAB for two specific models whose formations were similar to Equation (6). The specification of the computer used was a LAPTOP-5V7TD45J with a CPU of AMD Ryzen 5 3500U with Radeon Vega Mobile Gfx (2.10 GHz).…”

“…Machine vision plays the role of eyes for robots, aircraft technology, the Internet of things, and other mechatronics systems by providing the space coordinate figure [4]. With the location information of the target fruit, the picking robot can perform the supposed work [5,6]. As one of the machine vision methods for target distance assessment, monocular distance measurement has been applied in all sorts of fields, such as industrial robots [7], medical treatment [8], vehicle control [9], and fruit picking [10,11].…”

Research Progress on Low Damage Grasping of Fruit, Vegetable and Meat Raw Materials

“…Finally, a simulation analysis was conducted to evaluate the changes in contact force between the apple and different knuckles of the fingers, as well as the displacement and velocity changes of the fingers during the grasping process, verifying the adaptive grasping ability and the stability of the under-actuated arc-shaped finger. Due to the formation of a more fixed arc during the grasping process of picking small fruits such as cherry tomatoes and plums, Zhang et al [ 12 ] also designed a gripper with sine curve characteristic fingers based on biomimetic principles, as shown in Figure 4 b. The structural parameters of the gripper were determined while meeting the requirements of the grabbing range.…”

“… Design of biomimetic grippers based on the shapes of objects: ( a ) Underactuated end-effector for arc-shaped fingers; ( b ) gripper with sinusoidal curve characteristics [ 12 ]. …”

Defect Detection and Size Grading of Harvested Japanese Yam Using Computer Vision Technology

“…Therefore, there is an urgent need for reliable and efficient quality processing methods [2]. As an emerging and ongoing technology, computer vision has provided an accurate, highly efficient and non-destructive method for grading agricultural products and has become one of the most popular methods for external quality inspection of Yams [3].Although traditional techniques have been successfully applied in the field of Yam defect detection, these traditional methods are complex as they require several different steps and have significant limitations in accuracy and flexibility, which greatly limits their application in practical situations [4]. To overcome these limitations, deep learning provided practical solutions to some of the most challenging problems in recent years.…”